̽��ֱ�� of Cambridge - transport

Cambridge is forging a future for our planet

lw355 — Mon, 21 Oct 2024 09:00:36 +0000

Find out how Cambridge's pioneering research in climate and nature is regenerating nature, rewiring energy, rethinking transport and redefining economics – forging a future for our planet.

How road haulage is navigating the route to net zero

hcf38 — Fri, 11 Oct 2024 13:41:50 +0000

Responsible for 8% of world carbon emissions, can trucking clean up its act?

What does it take to make a better battery?

lw355 — Tue, 01 Oct 2024 08:20:28 +0000

Cambridge researchers are working to solve one of technology’s biggest puzzles: how to build next-generation batteries that could power a green revolution.

360-degree head-up display view could warn drivers of road obstacles in real time

sc604 — Wed, 20 Dec 2023 06:00:26 +0000

Researchers have developed an augmented reality head-up display that could improve road safety by displaying potential hazards as high-resolution three-dimensional holograms directly in a driver’s field of vision in real time.

Using machine learning to monitor driver ‘workload’ could help improve road safety

sc604 — Thu, 07 Dec 2023 07:48:29 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, working in partnership with Jaguar Land Rover (JLR) used a combination of on-road experiments and machine learning as well as Bayesian filtering techniques to reliably and continuously measure driver ‘workload’. Driving in an unfamiliar area may translate to a high workload, while a daily commute may mean a lower workload.

̽��ֱ��resulting algorithm is highly adaptable and can respond in near real-time to changes in the driver’s behaviour and status, road conditions, road type, or driver characteristics.

This information could then be incorporated into in-vehicle systems such as infotainment and navigation, displays, advanced driver assistance systems (ADAS) and others. Any driver-vehicle interaction can be then customised to prioritise safety and enhance the user experience, delivering adaptive human-machine interactions. For example, drivers are only alerted at times of low workload, so that the driver can keep their full concentration on the road in more stressful driving scenarios. ̽��ֱ��results are reported in the journal IEEE Transactions on Intelligent Vehicles.

“More and more data is made available to drivers all the time. However, with increasing levels of driver demand, this can be a major risk factor for road safety,” said co-first author Dr Bashar Ahmad from Cambridge’s Department of Engineering. “There is a lot of information that a vehicle can make available to the driver, but it’s not safe or practical to do so unless you know the status of the driver.”

A driver’s status – or workload – can change frequently. Driving in a new area, in heavy traffic or poor road conditions, for example, is usually more demanding than a daily commute.

“If you’re in a demanding driving situation, that would be a bad time for a message to pop up on a screen or a heads-up display,” said Ahmad. “ ̽��ֱ��issue for car manufacturers is how to measure how occupied the driver is, and instigate interactions or issue messages or prompts only when the driver is happy to receive them.”

There are algorithms for measuring the levels of driver demand using eye gaze trackers and biometric data from heart rate monitors, but the Cambridge researchers wanted to develop an approach that could do the same thing using information that’s available in any car, specifically driving performance signals such as steering, acceleration and braking data. It should also be able to consume and fuse different unsynchronised data streams that have different update rates, including from biometric sensors if available.

To measure driver workload, the researchers first developed a modified version of the Peripheral Detection Task to collect, in an automated way, subjective workload information during driving. For the experiment, a phone showing a route on a navigation app was mounted to the car’s central air vent, next to a small LED ring light that would blink at regular intervals. Participants all followed the same route through a mix of rural, urban and main roads. They were asked to push a finger-worn button whenever the LED light lit up in red and the driver perceived they were in a low workload scenario.

Video analysis of the experiment, paired with the data from the buttons, allowed the researchers to identify high workload situations, such as busy junctions or a vehicle in front or behind the driver behaving unusually.

̽��ֱ��on-road data was then used to develop and validate a supervised machine learning framework to profile drivers based on the average workload they experience, and an adaptable Bayesian filtering approach for sequentially estimating, in real-time, the driver’s instantaneous workload, using several driving performance signals including steering and braking. ̽��ֱ��framework combines macro and micro measures of workload where the former is the driver’s average workload profile and the latter is the instantaneous one.

“For most machine learning applications like this, you would have to train it on a particular driver, but we’ve been able to adapt the models on the go using simple Bayesian filtering techniques,” said Ahmad. “It can easily adapt to different road types and conditions, or different drivers using the same car.”

̽��ֱ��research was conducted in collaboration with JLR who did the experimental design and the data collection. It was part of a project sponsored by JLR under the CAPE agreement with the ̽��ֱ�� of Cambridge.

“This research is vital in understanding the impact of our design from a user perspective, so that we can continually improve safety and curate exceptional driving experiences for our clients,” said JLR’s Senior Technical Specialist of Human Machine Interface Dr Lee Skrypchuk. “These findings will help define how we use intelligent scheduling within our vehicles to ensure drivers receive the right notifications at the most appropriate time, allowing for seamless and effortless journeys.”

̽��ֱ��research at Cambridge was carried out by a team of researchers from the Signal Processing and Communications Laboratory (SigProC), Department of Engineering, under the supervision of Professor Simon Godsill. It was led by Dr Bashar Ahmad and included Nermin Caber (PhD student at the time) and Dr Jiaming Liang, who all worked on the project while based at Cambridge’s Department of Engineering.

Reference:
Nermin Caber et al. ‘Driver Profiling and Bayesian Workload Estimation Using Naturalistic Peripheral Detection Study Data.’ IEEE Transactions on Intelligent Vehicles (2023). DOI: 10.1109/TIV.2023.3313419

Researchers have developed an adaptable algorithm that could improve road safety by predicting when drivers are able to safely interact with in-vehicle systems or receive messages, such as traffic alerts, incoming calls or driving directions.

There is a lot of information that a vehicle can make available to the driver, but it’s not safe or practical to do so unless you know the status of the driver

Bashar Ahmad

Coneyl Jay via Getty Images

Head up display of traffic information and weather as seen by the driver

̽��ֱ��text in this work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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'Lightning McGreen' and 'Sustainable Hulk' lead Cambridge E-bus revolution

plc32 — Fri, 16 Jun 2023 14:17:29 +0000

Alongside the two famed children's animation and comic book-inspired characters, the names 'Greenhopper', 'Net-Zero Hero', 'Pollution Solution', ' ̽��ֱ��Peregreen Falcon', 'Eco Eddie' and ' ̽��ֱ��Green Clean Machine', were also chosen for the fleet from a selection offered by students from the ̽��ֱ�� of Cambridge Primary School in a bus naming competition.

̽��ֱ��competition invited students from the school in the ̽��ֱ��-built neighbourhood of Eddington to unleash their imagination. Participants were encouraged to consider factors such as the sustainability benefits and innovative features of the new buses in their naming choices. This initiative aimed to engage young minds in a fun and educational way, while also contributing to the enhancement of public transport within the local community.

Over the past few weeks, the competition captured the attention and enthusiasm of a large number of Eddington school children, attracting well over 100 entries. Their creativity and thoughtfulness were truly remarkable, making the selection process a challenging yet enjoyable task for the judging panel.

̽��ֱ��final selection was made by a panel of representatives from the ̽��ֱ�� of Cambridge and Whippet’s parent company, Ascendal Group. ̽��ֱ��panel carefully evaluated each entry and assessed the names based on originality, relevance, and the potential to resonate with the local community.

“We were overwhelmed by the incredible response from the young participants,” said Nicoletta Gennaro, Ascendal’s Group Head of Marketing. “ ̽��ֱ��names suggested by these talented children were not only impressive but also reflected their deep understanding of our community’s values and aspirations. We are thrilled to involve them in shaping the identity of our new electric buses.”

Winners of the competition received special recognition at a dedicated award ceremony at the ̽��ֱ�� of Cambridge Primary School, where they received prizes from representatives from Whippet and the ̽��ֱ��.

“We believe that involving the youth in important community projects like this fosters a sense of belonging and ownership,” added Mike Davies, Transport Manager at the ̽��ֱ�� of Cambridge. “Through their contribution, we hope to inspire future generations to actively participate in shaping the development of our city and how we move.”

Both Whippet and the ̽��ֱ�� of Cambridge would like to extend their sincere gratitude to all the participating students and staff at the ̽��ֱ�� of Cambridge Primary School for their invaluable contributions to the competition. ̽��ֱ��event marks a significant milestone in promoting creativity, community engagement, and the importance of sustainable public transport.

'Lightning McGreen' and the ' ̽��ֱ��Sustainable Hulk' will lead a new fleet of nine electric buses plying routes travelled by students and staff across the ̽��ֱ�� of Cambridge on the Universal bus route, scheduled to be put into service later this year by bus operator Whippet.

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London Underground polluted with metallic particles small enough to enter human bloodstrem

sc604 — Thu, 15 Dec 2022 15:55:12 +0000

̽��ֱ��London Underground is polluted with ultrafine metallic particles small enough to end up in the human bloodstream, according to ̽��ֱ�� of Cambridge researchers. These particles are so small that they are likely being underestimated in surveys of pollution in the world’s oldest metro system.

Machine learning algorithm predicts how to get the most out of electric vehicle batteries

sc604 — Tue, 23 Aug 2022 09:01:34 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, say their algorithm could help drivers, manufacturers and businesses get the most out of the batteries that power electric vehicles by suggesting routes and driving patterns that minimise battery degradation and charging times.

̽��ֱ��team developed a non-invasive way to probe batteries and get a holistic view of battery health. These results were then fed into a machine learning algorithm that can predict how different driving patterns will affect the future health of the battery.

If developed commercially, the algorithm could be used to recommend routes that get drivers from point to point in the shortest time without degrading the battery, for example, or recommend the fastest way to charge the battery without causing it to degrade. ̽��ֱ��results are reported in the journal Nature Communications.

̽��ֱ��health of a battery, whether it’s in a smartphone or a car, is far more complex than a single number on a screen. “Battery health, like human health, is a multi-dimensional thing, and it can degrade in lots of different ways,” said first author Penelope Jones, from Cambridge’s Cavendish Laboratory. “Most methods of monitoring battery health assume that a battery is always used in the same way. But that’s not how we use batteries in real life. If I’m streaming a TV show on my phone, it’s going to run down the battery a whole lot faster than if I’m using it for messaging. It’s the same with electric cars – how you drive will affect how the battery degrades.”

“Most of us will replace our phones well before the battery degrades to the point that it’s unusable, but for cars, the batteries need to last for five, ten years or more,” said Dr Alpha Lee, who led the research. “Battery capacity can change drastically over that time, so we wanted to come up with a better way of checking battery health.”

̽��ֱ��researchers developed a non-invasive probe that sends high-dimensional electrical pulses into a battery and measures the response, providing a series of ‘biomarkers’ of battery health. This method is gentle on the battery and doesn’t cause it to degrade any further.

̽��ֱ��electrical signals from the battery were converted into a description of the battery’s state, which was fed into a machine learning algorithm. ̽��ֱ��algorithm was able to predict how the battery would respond in the next charge-discharge cycle, depending on how quickly the battery was charged and how fast the car would be going the next time it was on the road. Tests with 88 commercial batteries showed that the algorithm did not require any information about previous usage of the battery to make an accurate prediction.

̽��ֱ��experiment focused on lithium cobalt oxide (LCO) cells, which are widely used in rechargeable batteries, but the method is generalisable across the different types of battery chemistries used in electric vehicles today.

“This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows us to capture the health of the battery beyond a single number, and because it’s predictive,” said Lee. “It could reduce the time it takes to develop new types of batteries, because we’ll be able to predict how they will degrade under different operating conditions.”

̽��ֱ��researchers say that in addition to manufacturers and drivers, their method could be useful for businesses that operate large fleets of electric vehicles, such as logistics companies. “ ̽��ֱ��framework we’ve developed could help companies optimise how they use their vehicles to improve the overall battery life of the fleet,” said Lee. “There’s so much potential with a framework like this.”

“It’s been such an exciting framework to build because it could solve so many of the challenges in the battery field today,” said Jones. “It’s a great time to be involved in the field of battery research, which is so important in helping address climate change by transitioning away from fossil fuels.”

̽��ֱ��researchers are now working with battery manufacturers to accelerate the development of safer, longer-lasting next-generation batteries. They are also exploring how their framework could be used to develop optimal fast charging protocols to reduce electric vehicle charging times without causing degradation.

̽��ֱ��research was supported by the Winton Programme for the Physics of Sustainability, the Ernest Oppenheimer Fund, ̽��ֱ��Alan Turing Institute and the Royal Society.

Reference:
Penelope K Jones, Ulrich Stimming & Alpha A Lee. ‘Impedance-based forecasting of lithium-ion battery performance amid uneven usage.’ Nature Communications (2022). DOI: 10.1038/s41467-022-32422-w

For more information on energy-related research in Cambridge, please visit Energy IRC, which brings together Cambridge’s research knowledge and expertise, in collaboration with global partners, to create solutions for a sustainable and resilient energy landscape for generations to come.

Researchers have developed a machine learning algorithm that could help reduce charging times and prolong battery life in electric vehicles by predicting how different driving patterns affect battery performance, improving safety and reliability.

This method could unlock value in so many parts of the supply chain, whether you’re a manufacturer, an end user, or a recycler, because it allows us to capture the health of the battery beyond a single number

Alpha Lee

Monty Rakusen via Getty Images

People charging their electric cars at charging station in York

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

Yes